Asthma control and clinical remission in severe eosinophilic asthma (SEA) after 2 years of benralizumab treatment: prospective real-world data from XALOC-2
Bibliographic record
Abstract
Background: Prospective long-term real-world data on the effectiveness of benralizumab on asthma symptom control in patients (pts) with SEA are lacking. Aim: Examine real-world effectiveness of benralizumab over 112 weeks (Wks). Methods: XALOC-2 comprises prospective, real-world studies of pts with SEA treated with benralizumab in Belgium, Canada, Germany and Switzerland. This 112 Wk integrated analysis assessed Asthma Control Questionnaire (ACQ) scores, annualised asthma exacerbation rate (AAER) and 3-component clinical remission (no exacerbations, no maintenance oral corticosteroid use, asthma symptom control [ACQ<1.5]). Results: Of 534 pts (median age 58 years; 49% female), median (IQR) ACQ score at baseline was 3.0 (2.2–3.8), decreasing to 1.2 (0.6–2.3; Wk 56) and remaining stable at 1.2 (0.5–2.2) to Wk 112 (Fig 1). Clinically meaningful improvements (minimal clinically-important difference threshold≥‒0.5) occurred in 58% of pts at Wk 1, increasing to 79% at Wk 112. AAER decreased by 85% from baseline to Wk 112. Overall, 1% met remission criteria at baseline vs 41% at Wk 112, with remission rates lower in pts with obesity than with normal BMI (42%/49%). Conclusion: Real-world patients with SEA showed early and sustained symptom control over 2 years of benralizumab treatment. More than 4 out of 10 patients were in remission after 2 years of treatment. erj;66/suppl_69/PA5705/F1 F1 F1
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".